With advantages including low cost, high precision and wide application (indoors and outdoors) and so on, a locating system based on a Wireless Local Area Network (WLAN) has achieved great success in location-based services, such as aspects including emergency rescue, intelligent transportation and indoor positioning and navigation and so on. However, two problems as follows still need to be solved. The first problem is that the locating precision of the WLAN is seriously deteriorated by fluctuation of a Received Signal Strength (RSS) caused by factors including multi-path interference and so on, and the second problem is that the WLAN can be hardly located due to the lack of an Access Point (AP) in an area not covered by the AP.
Many methods have been proposed to solve the problems, which can be divided into the following two types.
The first type is a WLAN locating system based on a time diversity and a probability distribution model. The basic idea of the WLAN locating system based on a time diversity and a probability distribution model is that a plurality of samples of RSSs is acquired by using a time diversity at a fixed location in a locating area, a probability distribution model of the RSSs is established according to information of the plurality of samples and stored in a characteristic database; in a locating phase, a mobile target acquires the plurality of samples of the RSSs by using the time diversity, and calculates an average value of the samples to acquire a stable RSS so as to perform locating. Since a large amount of time is consumed by the time diversity, a locating delay is increased, thereby real-time locating can be hardly implemented, the WLAN locating system based on a time diversity and a probability distribution model cannot be used in mobile locating, and the database will fail after an environment changes.
The second type is a WLAN locating system based on Kalman filtering. The basic idea of the WLAN locating system based on Kalman filtering is that location estimation of a mobile target is acquired by using a WLAN locating algorithm first, then a state equation and an observation equation of a Kalman filter is constructed by using the track continuity of the mobile target or by assuming that a velocity of the mobile target is in a certain range, thereby filtering location estimation of a user. Although such a method improves the locating precision of the WLAN locating system, adaptive filtering can be hardly implemented due to the fact that the velocity of the mobile target has been set in advance, thereby limiting practical application, and the problem that WLAN locating fails caused by the lack of an AP cannot be solved.